The effectiveness of fiscal policy: the role of age structure, a case study of China

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The effectiveness of fiscal policy:

the role of age structure,

a case study of China


Statement of Originality

This document is written by Student [He, Zhentong] who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

UvA Economics and Business is responsible solely for the supervision of completion of the work and submission, not for the contents.



This paper studies the effect of ageing on fiscal policy in China. PVAR is established on stable data from China to calculate the fiscal multiplier. The model was tested to be stable, and its results are presented as an impulse response graph. The following conclusions are drawn increased government expenditure positively impacts future government expenditure and tax revenue. The positive effect of the mild ageing group is more robust than that of the degree ageing group. Because slightly ageing areas spend more on promoting employment and investment, severely aged areas spend more on social health care and old-age security.

The increase in government expenditure also promoted a positive rise in total output, while the ageing had no noticeable effect on the change of the entire production.

Finally, ageing will lead to a reduction in the multiplier of government spending. The removal of severe ageing is more pronounced, caused by reducing the labour force.

According to the conclusion๏ผš the negative impact of ageing on fiscal policy is mainly through the proportion of pension expenditure to squeeze other policy budgets to affect the sustainability of fiscal policy. This paper puts forward three policy suggestions:

ยท Improve the re-employment level of the elderly population.

ยท Improve society's overall labour participation rate.

ยท Develop a diversified pension structure.


: government spending, fiscal multiplier, PVAR, age structure



This paper studies the effect of ageing on the effectiveness of the fiscal policy and the research object in China. This section will explain the concept of the fiscal multiplier, an essential measure of fiscal policy effectiveness, and the practical reasons influencing the fiscal multiplier. At the same time, it will also analyze the global ageing trend and the ageing situation in China. This paper chooses to study the increasingly severe trend of ageing. Finally, this paper will elaborate on the development characteristics of China and the critical reference significance of Chinese data.

Fiscal policy is an essential tool for the government to macro-control the economy.

The fiscal multiplier includes the government spending, tax, and balanced fiscal multiplier. Used to explain how changes in government spending and taxes increase or decrease GDP. It is also an essential tool for measuring the effectiveness of fiscal policy.

Empirical data and previous studies mainly focus on the persistence of spending changes, financing methods, monetary policy responses and labour market tightening.

All these factors will affect the fiscal multiplier. Nevertheless, this paper chooses ageing, which has a considerable impact on fiscal policy, different from the aforementioned factors.

Ageing is an important demographic trend facing most countries globally and is worth studying. According to the UN's World Population Prospects: According to the 2019 Revision, 9 per cent of the world's population was over 65 years old in 2019, but this figure will rise to 16 per cent by 2050, which means that one in six people in the world will be older. In Europe and the Americas, ageing is even more severe, and the figure is projected to reach 25 per cent. While some developing countries face ageing population problems, China is one of them.

According to the ageing classification standard of the World Health Organization, China has entered a mildly ageing society since 2002, when the proportion of the elderly population over 65 years old exceeds 7% as the threshold for entering an ageing society.

In addition, the ratio of the elderly population continues to increase. The growth rate has been maintained at a high level, and the continuous transition to a severely ageing society. China's population ageing rate is also fast. The Chinese population is also ageing faster. In the 1950s, China's population ageing rate increased less fast than the world average. The rate of population ageing accelerated in the early 21st century and has overtaken the world's developed economies with higher population ageing.


According to data from China's seventh population census in 2020, The country is getting older. The Strategic Research Report on Actively Coping with Population Ageing 2021, released by the Chinese Academy of Social Sciences, summarizes five characteristics of China's ageing population. First, the ageing of the population is increasing, and the trend of ageing is noticeable. The population aged 60 and above reached 264.02 million, accounting for 18.70 per cent. 19.064 million, or 13.50 per cent, were old 65 and above. Second, the ageing rate has accelerated. Compared with 2010, the proportion of people aged 60 and above increased by 5.44 percentage points. This figure increased by 2.51 percentage points over the previous decade. Third, the gap between population ageing and population sharing is expanding rapidly, especially in rural areas. The proportion of people aged 60 and 65 in rural areas was 23.81% and 17.72%, respectively, 7.99 and 6.61 percentage points higher than those in urban areas.

Fourth, regional differences in ageing are widening. The gap between the regions with the highest proportion of people aged 65 and over and the lowest was nearly 12 percentage points. The regional difference index of population ageing increased from 0.14 in 2010 to 0.17 in 2020. Fifth, the region with a low economic development deviate from economic common sense and has a high level of population ageing. The correlation between people aged 60 and over and regional GDP per capita ranking in 2020 is only 0.310. at the same time, the exact correlation between people aged 65 and over is only 0.250.

The United Nations predicts that China's ageing population will reach its peak in the middle of the 21st century and become the country with the fastest ageing population globally. In this paper, ageing is a common trend facing the world, and China's ageing level is also deepening, making this topic valuable for research.

At the same time, China is picked as the research sample and is also of great value.

In his study, Ethan Ilzetzki (2013) found that government expenditure shocks largely depend on critical national characteristics, such as a higher level of development, a fixed exchange rate regime, a lower degree of trade openness, and less public debt. For example, China has a better development level, a more conservative exchange rate regime and less public debt. At the same time, because China's political system is different from the separation of powers in western countries, the Chinese government's macro-control ability is among the best in the world. The study of Chinese data can better analyze the impact of population ageing on the fiscal multiplier under the strict implementation of macro policies and then analyze the effectiveness of fiscal policies.


In addition, as a developing country, China's economic development level is lower than that of developed countries in Europe and the United States. However, its economic growth rate is relatively high, a representative feature of China. Compared with other developed countries such as European and American countries and Japan, the study on the impact of China's ageing trend on the effectiveness of fiscal policies provides a reference basis for other developing countries. Therefore, the study on China's ageing problem is representative and meaningful.

Literature review

Through reading relevant literature, this paper sorted out research ideas on the impact of ageing on fiscal policies and introduced relevant research results in this part.

First, the empirical conclusion obtained by some scholars based on the statistical results of data in a specific period is listed, and we know that ageing will weaken the effect of fiscal policy. Secondly, the relevant reasons analysed by other scholars are summarised.

Finally, various measures proposed by other scholars to solve this problem are outlined, which will serve as one of the bases for this paper to put forward policy suggestions.

According to the research results of Puhakka (2005), Ewijk (2006) and Nakahigashi & Yoshino (2017), population ageing is bound to impact consumption, savings, investment, and taxation, and thus significantly reduce the potential effects of macro-control policies. Henrique S. Basso and Omar Rached (2021) supported this conclusion with historical data. The ageing of the US population between 1980 and 2015 resulted in a 38% decrease in the multiplier of national government spending. The Asian Development Bank (ADB) study shows that the increasing number of retirees undermines the effectiveness of the fiscal and monetary policy. Jiro Honda & Hiroaki Miyamoto (2021) analysed the impact of ageing in combination with the economic cycle. They concluded that population ageing does not affect the output effect of fiscal expenditure shock in the expansionary period. With the population ageing in the recession period, the output effect of government expenditure shock weakens. Thus, the conclusion can be summarised as the increase in the ageing level will dilute the impact of fiscal policy.

Different scholars put forward different views on why ageing weakens the effect of fiscal policy. Andersen (2012) believes that the increasing ageing of the population leads to an increasing financial pension burden, which affects the sustainability of the government's fiscal space expansion, thus affecting the follow-up effect of fiscal


policies. The Organization for Economic Co-operation and Development (OECD) released a report pointing out that the increasingly severe ageing will inevitably lead to a growing government debt, which will push up interest rates to a certain extent and impact financial stability. It also leads to the reduction of government spending in fiscal policy, reducing the effect of fiscal policy. Li Jianqiang (2018) divides the impact of population ageing into two categories. One is the direct negative impact on economic growth, which mainly refers to reducing the labour force. Second, the space for implementing fiscal policies is squeezed, manifested in the increase of pension expenditure in the fiscal spending, squeezing other policy budgets. Therefore, the weakening of fiscal policies by ageing can be summarised as the labour force's decrease in fiscal revenue and increased government pension burden. Then, the heavy burden increases government debt and squeezes other policy budgets, which is not conducive to the sustainability of fiscal policies.

Some scholars have evaluated and analysed the existing solutions to solve the problems of increasing financial burden and insufficient pension caused by ageing. Liu Xueliang (2014) studied from the perspective of the sustainability of fiscal policy and found that extending the retirement and pension age and lowering the replacement rate of pension insurance can effectively make up for the pension gap. However, the effect of increasing the return rate of pension insurance investment and the fertility rate is limited. Li Jianqiang (2018) proposed that improving the labour participation rate of the elderly population is an effective way to increase the labour supply. Hiroshi Morita (2022) also came to the same conclusion after studying Japan that labour supply channels play a significant role in the effectiveness of fiscal policies. Workers could benefit from increased disposable income due to the government spending shock. When the ageing trend shows up, one of the valuable solutions is improving the labour force participation rate of the elderly. This policy can enhance the effectiveness of government expenditure shock.

Description of Model

This part will first introduce why the model we choose is suitable for analyzing the impact of ageing on fiscal policies. Then, the steps of model construction and verification were introduced and presented the results.

Panel Vector Autoregression model (PVAR) is a model based on Panel data that can effectively solve individual heterogeneity and comprehensively consider individual


and time effects. The policy implications of regional economic differences can be formed by depicting individual time performance.

This paper uses the panel vector autoregression (PVAR) model (Love 2016).

Compared with the traditional VAR model, this model sets all variables as endogenous variables, which is suitable for studying economic problems with individual differences.

The expression of the PVAR model is:

Y๐‘–๐‘ก = ๐ด๐‘–+ โˆ‘ ๐ด๐‘—ร— ๐‘Œ๐‘–๐‘กโˆ’๐‘—+ ๐œ๐‘ก+ ๐œ€๐‘–๐‘ก

๐‘˜ ๐‘—=1

In the expression, t for the year, i for different variables, the lag order number is k.

๐ด๐‘– shows the individual effect. ๐ด๐‘— is the lag variable coefficient matrix. Y๐‘–๐‘ก that contains all the endogenous variables in the model column, ๐‘Œ๐‘–๐‘กโˆ’๐‘— is endogenous variable j order lag. vector ๐œ๐‘ก as the time effect, ๐œ€๐‘–๐‘กthe random disturbance term.

The next step will be sorted into panel data format, and after input into STATA software, the stationarity of the data needs to be tested by unit root. For economic data, most of the original data are unstable. If the tested data is stable, modelling can be carried out directly. If not, this paper chooses to process the unstable data into stable data by taking the natural logarithm and first-order difference and then modelling.

AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and HQIC (Hannan-Quinn Information) should be used Criterion) to judge the lag order of the model. The smaller, the better. The calculation method is as follows:

๐ด๐ผ๐ถ = โˆ’2๐‘™ ๐‘‡ +2๐‘›

๐‘‡ ๐‘†๐ถ = โˆ’2๐‘™

๐‘‡ +๐‘›๐‘™๐‘›๐‘‡ ๐‘‡

Where n=k ๏ผˆ d+pk ๏ผ‰ summarizes estimated parameters, k is the number of endogenous variables, T is the sample length, and d is the number of exogenous variables. The formula of L is as follows:

๐‘™ = โˆ’๐‘‡๐‘˜

2 (1 + ๐‘™๐‘›2๐œ‹) โˆ’๐‘‡

2ln (๐›ดฬ‚)

In the PVAR model, the impulse response function (IRF) is used to analyze the dynamic influence on the system when the model is subjected to a particular impact, that is, when an error term changes.

Description of Data

This part will introduce the pre-processing of data acquisition, screening and


modelling, including data sources, selected data periods and reasons for excluding special periods. Also, this part includes the actual data obtained from provinces and the treatment of missing values and outliers. Finally, the basis of grouping data according to the ageing situation and the final grouping result is introduced.

First, the data source, this paper USES data from the National Bureau of Statistics website ( and provincial bureau of the statistics web site, China's economic and social big data research platform website, China statistical yearbook, China's population statistics yearbook and the provinces statistical yearbook.

The second is the selection of the data cycle. This paper selects data from 2002 to 2019 for research. According to the World Health Organization's ageing classification criteria, the decision was based on China's officially entering an ageing society in 2002 when the elderly population exceeded 7 per cent. Data from COVID-19 since 2019 were excluded from ruling out the significant impact of unnatural fluctuations in the proportion of older people due to the higher mortality rate of COVID-19 for older people and increased government spending and tax breaks to protect businesses in response to COVID-19.

This paper selects 29 provinces in China (excluding Hong Kong, Macao, and Taiwan regions, Tibet Autonomous Region, and Chongqing Municipality). The data of the excluded regions are seriously missing. Missing values appeared in the data variables of the provinces involved in the analysis. This paper uses the arithmetic average of the two years' data to supplement the missing years.

The next step is the grouping of provinces. Based on Hiroshi Morita's (2022) grouping method of Japanese ageing research, this paper takes the data from 2002. It divides the 29 provinces studied into the severe and mild ageing groups according to the average ageing degree. The critical value is standard by China's Office of Ageing set to distinguish severe from mild, and the old-age dependency ratio is 12%. There are fifteen provinces in the severe ageing group, including Sichuan province, Shanghai city, Jiangsu province, Anhui, Hunan province, Shandong province, Liaoning province, Zhejiang province, Tianjin city, Hubei province, Guangxi Zhuang Autonomous Region, Guizhou province, Beijing city, Hebei province, and Shaanxi province. There are nine provinces with mild ageing groups: Henan province, Jiangxi province, Fujian province, Hainan province, Inner Mongolia Autonomous Region, Jilin province, Gansu province, Heilongjiang province, Yunnan province, Shanxi province, Guangdong province, Ningxia Hui Autonomous Region, Xinjiang Uygur Autonomous Region and Qinghai



Description of variables

The following sections describe the five variables used to build the model from the perspective of the concept of variables and the information they contain. Then, given the calculation method for the fiscal multiplier. Then we introduce the logarithmic difference method and its economic significance for obtaining static data.

After data grouping, the index system of PVAR was established according to the above theoretical analysis. The first is to describe the ageing level using the old-age dependency ratio index, also known as the old-age dependency coefficient, expressed in percentage, indicating the number of older people supported by every 100 working- age people. The calculation formula is the ratio of middle-aged and older people to the number of working-age people in a particular area.

Taking Blanchard and Perotti's (2002) and Hiroshi Morita's (2022) studies for reference, this paper uses local fiscal general budget expenditure and local fiscal tax revenue as variables to study the effectiveness of fiscal policies. Expenditures in the local general budgets include expenditures on public services, national defence, public security, education, science and technology, culture, sports and media, social security and employment, medical and health care, environmental protection, urban and rural community affairs, agriculture, forestry and water affairs, and transportation. The tax revenue index includes 18 tax revenues, including value-added tax, consumption tax, business tax, enterprise income tax, and individual income tax, covering all tax categories.

For the fiscal multiplier, China's National Bureau of Statistics released not provinces and regions of the fiscal multiplier. The specific values will be calculated from the results of the PVAR model. The calculation method refers to Valerie A.

Ramey (2019), and the calculation process will be explained in detail later.

Thus, the data of all endogenous variables involved in model construction are obtained, and their expressions are summarized in Table 1. To better eliminate heteroscedasticity and unify the economic significance of variables so that they can be changed from absolute change to relative change, this paper takes the natural logarithm of all original data and then carries out the first-order difference. Finally, the logarithmic and differential data dlnOLD, dlnSPE, dlnTAX and dlnGDPare obtained.


Table 1 Description of model original data variables.

Descriptive statistics

Before modelling, descriptive statistics are made for the original data without a logarithmic difference to analyze the average status, dispersion degree, and maximum and minimum values.

Table 2 Descriptive statistics of mildly aged area


Average 11.39 2516.88 907.15 14289.35 Median 11.20 1639.46 516.23 8698.15 The standard

deviation 2.01 2602.98 1398.41 18698.81

Min 7.00 92.26 15.82 443.70

Max 18.10 17297.85 10063.95 124369.70 Table 3 Descriptive statistics of heavily aged area


Average 14.74 3654.03 1775.16 25466.86

Median 14.30 3208.28 1330.06 21092.75

The standard deviation 2.66 2734.48 1588.07 19430.48

Min 10.30 265.21 96.62 2621.10

Max 23.80 12573.55 7339.59 116364.20

The following is a descriptive statistic of the data of the two groups. The data characteristics are mean, median, standard deviation, maximum and minimum. The elderly support of severely aged areas is larger than the average, and the average fiscal expenditure, tax revenue and regional GDP of severely aged areas are larger than those of mildly aged areas.

Standard deviation can describe the dispersion of data, and the larger the standard Indicator variables Specific indicators unit

OLD Ageing level Old-age dependency ratio


SPE Fiscal policy

Expenditures in the general budgets of local governments

One hundred million yuan

TAX Fiscal policy Local fiscal tax revenue One hundred million yuan

GDP Real output Gross regional product One hundred million yuan

FIS Fiscal multipliers Government spending multiplier



deviation is, the worse the concentration of data is. It can see that the degree of ageing in the mild ageing group is more concentrated than in another group. At the same time, the data in the severe ageing group are divided, which is reflected in the maximum and minimum values. The intra-group data difference of the severely ageing group is more significant than in another group. In the three aspects of government expenditure, tax revenue and actual output, the standard deviation of the severely ageing group are more significant than that of the mildly ageing group, indicating that the data in the severely ageing group is more dispersed than the data in another group.

Model construction and testing

This section describes how the model was built, the critical tests performed, and the results obtained. The pulse response diagram obtained by the model is also presented. We analyse the trend and characteristics of the impulse response graph. The reasons for this result are described based on the conclusions obtained in the literature review.

In this paper, the data from 2002 to 2019 were first studied. Furthermore, the IPS method was used to carry out a stationarity test for the data. At the 95% confidence level, the P-values of all variables were less than the critical value of 0.05, indicating that the unit root test under the IPS method could be passed. The optimal lag order was selected according to AIC, BIC, and HQIC criteria. The lag order of the severe and mild ageing groups was 3.

The PVAR model is re-established by software after the definite lag order. Then, the PVAR model is tested for model stability, and the characteristic graph of the adjoint matrix is drawn. All the points of the three groups of data fall within the unit circle, indicating that the model is stable.

In the next step, the impulse response was studied, and the impulse response graph was made for the five variables to get the results for the severe and mild ageing groups.

The vertical axis describes the direction and magnitude of the impact, and the horizontal axis describes the duration of the impact. The unit is years, showing a total of 10 years.

Figure 1 shows the empirical impulse responses of government spending to a positive government spending shock for the two groups. Under the same positive government spending shock, Figure 2 is the response from tax revenue. Figure 3 includes the response from the output. The red line is the severely ageing group, and the orange line is the mildly ageing group.


Figure 1๏ผšResponses of government spending to a positive government spending shock

Figure 2๏ผš Responses of tax revenue to a positive government spending shock

Figure 3๏ผš Responses of output to a positive government spending shock

The results of the figures are analyzed below. Firstly, it can be seen from Figure 1 that the effect of the first positive government expenditure on future government expenditure is increased. The decay rate of government spending shocks is more gradual for low-level shocks and faster for high-level shocks. Based on the studies of other scholars and the actual situation in China, the reason is that the composition structure of government expenditure is different in regions with different ageing degrees. Government spending in mildly ageing regions promotes employment and invests more in natural industries that promote employment, which can better sustain


economic growth. However, governments in severely ageing regions invest more government expenditures in social security, which has a poor driving effect on subsequent economic growth.

Figure 2 shows what happens to tax levels after a government spending shock. We can see taxes and government spending also maintain a positive relationship, and the duration of this effect even for ten years. By comparing the data of different ageing area groups, the tax revenue of mildly ageing areas has a more robust positive response range.

In mild ageing areas, employment promotion to provide jobs, and increase the residents' disposable income, is a way to keep the number of taxpayers, from individual income tax, enterprise income tax, and consumption tax, and a series of level provides tax sources. However, severe ageing areas of social security expenditure contain more health and pension to tax, such as the poor ability of the public product, therefore the growth rate of tax as mild ageing.

Figure 3 shows that government spending increased led to a positive increase in total output, with similar growth in mild and severe regions. Growth in total output declines faster, with an impact lasting about five years. Thus, can analyze whether the actual output is affected by positive government spending but not by ageing.

To obtain the GDP response corresponding to the unit government expenditure change. This paper conducts the following processing for the data in FIG. 1 and FIG.

3. Firstly, the response time of Figure 3 and Figure 5 is close to 5 years, so the cycle is five years. We summed up the response of spending and GDP to get the total increase in spending and the total change in GDP throughout the horizon. In the next step, we calculate the ratio of total GDP change to total government spending change. It reflects the GDP response to a unit of government spending. Furthermore, this is the fiscal multiplier.

Finally, this period's ten-year cumulative fiscal multipliers are obtained through the above method. The fiscal multiplier is 0.755 for the highly ageing group and 1.084 for the mildly ageing group. The slightly older group had a higher fiscal multiplier, suggesting that each unit of government spending translates into more output. However, the result of the highly ageing group is not only lower than that of the mildly ageing group but also less than 1, indicating that each unit of government expenditure cannot even increase the same output, and more government expenditure is consumed in the pension, rather than increasing the total social production. The government expenditure multiplier decreases with the ageing process, and the decrease is more evident in areas


with severe ageing. The government spending multiplier measures how much higher government spending contributes to the increase in total output, and ageing diminishes that boost. The reason is that due to the characteristics of the elderly, they no longer work but can invest and consume. According to experience, although government spending will stimulate investment and consumption to increase to drive the output increase, labour reduction will offset this effect. Moreover, because of an ageing workforce, less lead to a drop in the government spending multiplier.

Conclusion and policy implications

According to the model established in this paper, it can be concluded that the ageing process reduces the multiplier of government expenditure. This conclusion is consistent with the conclusions of Basso and Rachedi (2020), Honda and Miyamoto (2020), and Miyamoto and Yoshino (2020). The impact of ageing is mainly through the proportion of pension expenditure to squeeze the budget of other policies, which affects the sustainability of fiscal policy. According to Sun Yongyong, of the World Social Security Research Center at the Chinese Academy of Social Sciences, in an interview, China's pension spending varies widely among provinces. The expenditure growth rate was about 21 per cent, with Shanghai spending more than 100 billion yuan and Xizang spending the least, only 1.5 billion yuan. The spending growth rates also varied widely, with Nearly 40 per cent in Xizang and 15.9 per cent in Tianjin. From the treatment perspective, the pension has been increasing rapidly in recent years, but the annual growth rate is declining. Compared with the minimum national public finance expenditure in 2022, the accumulated year-on-year growth rate of 5.9%, the pension growth rate in recent years is much higher than the growth rate of government expenditure.

In macroeconomics, the primary effect of population ageing is that it will profoundly change the relationship between the distribution of national income and the allocation of economic resources. In the long run, the ageing population reduces the labour supply. The widening of the overall economic life cycle gap makes economic growth lack momentum, and the overall social welfare level continues to decline. In the short term, increasing the financial pension burden reduces fiscal policy room and weakens the government's ability to implement counter cyclical fiscal policy. In policy evaluation, the decline of fiscal multiplier conceals fiscal initiative, magnifies its crowding-out effect, and damages the quality of fiscal performance. On the monetary


policy side, population ageing may bring deflationary pressure and limit monetary policy's ability to stimulate aggregate demand. It forces central banks to take more aggressive measures to achieve the same effect and directly expose them to higher macroeconomic risk exposure, enhancing financial vulnerability.

The Chinese government is also taking active measures to deal with the ageing trend. The guidelines of the CPC Central Committee and The State Council on Strengthening Work on Ageing in the New Era, released in 2021, set out the goals for the future. The countermeasures can be divided into two aspects: the measures to provide pension and medical security for the elderly population directly; the other is the measures to deal with ageing to promote social and economic development. This paper will briefly evaluate these measures based on the effectiveness of other literature on coping with ageing.

The first is the Chinese government's pension and medical security measures, which specifically include developing the model of community pension for the elderly who can take care of themselves and providing subsidized canteen and door-to-door cleaning services for the elderly in communities. For the elderly disabled, the institutional pension model will be developed to replace the traditional family pension model. Improve the old-age security system, expand the coverage of old-age insurance, and vigorously develop the third pillar of old-age insurance in the community by regularly carrying out screening, prevention, and other activities of chronic diseases.

These measures can solve the problem of living care for the elderly and provide many cleaning, nursing, and medical-related jobs, create new demand and supply, and promote economic development.

The second is the social measures to cope with ageing, mainly reflected in the population measures. China used to limit the number of births, and a couple could only raise one child. With the severe ageing, China relaxed this limit and allowed a couple to have three children. At the same time, the policy of postponing retirement time is also under development but has not been implemented yet. These policies aim to provide more workers, but the short-term effect is not apparent. It takes more than a decade for the new population to enter the labour force. Other policies are therefore needed in the short term. The policy recommendations put forward in this paper aim to solve short-term problems.

The research of this paper is of reference signs in the policy-making process.

Based on the research results of other scholars and the author's viewpoints, the


following policy suggestions are put forward. First, increase the labour force participation rate of the elderly and encourage the participation of the elderly who can work, especially those with professional skills and knowledge. It helps increase labour force participation and compensate for the decline in the labour force.

Second, increase labour force participation in society, especially among women, and attract immigrants needed by the labour market. Retired people no longer pay taxes, increase personal income tax and social output, and increase the room for fiscal policy.

To achieve this goal, relevant laws and policies need to be formulated to protect the rights and interests of female employees and workers of different nationalities.

Third, we need to develop a diversified pension structure. China's pension structure, personal savings, children's support, and government-provided pension insurance are the most critical channels. In contrast, the development of commercial pension insurance is far less than that of European and American countries. China should learn from the experience of other countries and develop a more diversified pension structure. By developing commercial pension insurance, pension fund accounts and other products, China should socialize pension costs to reduce the burden of the government's pension and thus leave more space for other policies.


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